Contribution of the Landscape Evaluation in the Study of the Impact on Environment: Application of the Hydro-Québec Method on the Technical Landfill Center of Hamici, Tipaza (Algeria)
Bibliographic record
Abstract
The development and operation of Technical Landfill Centers (TLC) for urban solid waste lead to significant landscape alterations and have a negative impact on its overall image. However, the Environmental Impact Assessment (EIA) conducted for these TLCs in Algeria do not currently consider the impact of this activity on the landscape as a determining factor for validating the implementation of the landfill site. It is limited, instead, to some mitigation measures. This work addresses the importance of taking into account the impact of TLCs on the visible landscape through EIAs. In this context, the planned landscape integration measures within the scope of the EIA conducted by the Ministry of Environment for the urban solid waste landfill site of Hamici, situated 29 km from the capital Algiers, were examined. In addition, an evaluation the TLC’s impact on the visible landscape after its construction and operation was implemented using the Hydro-Québec method. The results show a high visual impact of the TLC on the landscape unit receiving the landfill cells, a moderate impact on the unit receiving the TLC buildings, and a minor impact on the unit hosting the human settlement, which has the largest number of potential observers in the area. In the light of these findings, it is imperative to integrate landscape evaluation as an operational phase through EIAs before making decisions regarding the siting of landfill sites. This is essential for the purpose of preserving the image of the environment conveyed by the visible landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".